Abstract This special issue of the “Journal of the Association for Information Science and Technology” examines conceptual models as products of, and tools for, critical inquiry in Information Science (IS). The papers included in this issue present diverse perspectives on how conceptual models impact sociotechnical systems, spanning topics such as knowledge organization, representation, and information system design. Key themes include the intersection of model development with ethical considerations, the historical and future implications of conceptual modeling decisions, and the potential for conceptual models to address issues of power, representation, and justice in emerging technologies. This introduction situates the contributions within broader discussions of conceptual modeling in IS and highlights the field's unique approach to reflexive critique and sociotechnical analysis.

Despite the rapid growth of digital scholarship in the humanities, most existing humanities research infrastructures lack adequate support for the creation, management, sharing, maintenance, and preservation of complex, networked digital objects. Research Objects (ROs) have mainly been applied to scientific research workflows, but the RO model and parallel approaches have gained enough uptake in the humanities to suggest their potential to undergird sustainable, networked humanities research infrastructure. This paper reviews several compelling applications in the humanities of RO and closely related models in platforms for data sharing, computational text analysis, collaborative annotation, digital and semantic publishing, and in domain repositories. The paper identifies challenges confronting the broad application of ROs in the humanities-which challenges will confront any emergent model for humanities data-or workflow-packaging and publication-and suggests implications for implementations in humanities cyberinfrastructure.

Collections containing a large number of short documents are becoming increasingly common. As these collections grow in number and size, providing effective retrieval of brief texts presents a significant research problem. We propose a novel approach to improving information retrieval (IR) for short texts based on aggressive document expansion. Starting from the hypothesis that short documents tend to be about a single topic, we submit documents as pseudo-queries and analyze the results to learn about the documents themselves. Document expansion helps in this context because short documents yield little in the way of term frequency information. However, as we show, the proposed technique helps us model not only lexical properties, but also temporal properties of documents. We present experimental results using a corpus of microblog (Twitter) data and a corpus of metadata records from a federated digital library. With respect to established baselines, results of these experiments show that applying our proposed document expansion method yields significant improvements in effectiveness. Specifically, our method improves the lexical representation of documents and the ability to let time influence retrieval.